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1.
Exploring the feasibility of generative AI in persona research : a omparative analysis of large language model-generated and human-crafted personas in obesity research
Urška Smrke, Ana Dimič, Nejc Plohl, Izidor Mlakar, 2025, izvirni znanstveni članek

Opis: This study investigates the perceptions of Persona descriptions generated using three different large language models (LLMs) and qualitatively developed Personas by an expert panel involved in obesity research. Six different Personas were defined, three from the clinical domain and three from the educational domain. The descriptions of Personas were generated using qualitative methods and the LLMs (i.e., Bard, Llama, and ChatGPT). The perception of the developed Personas was evaluated by experts in the respective fields. The results show that, in general, the perception of Personas did not significantly differ between those generated using LLMs and those qualitatively developed by human experts. This indicates that LLMs have the potential to generate a consistent and valid representation of human stakeholders. The LLM-generated Personas were perceived as believable, relatable, and informative. However, post-hoc comparisons revealed some differences, with descriptions generated using the Bard model being in several Persona descriptions that were evaluated most favorably in terms of empathy, likability, and clarity. This study contributes to the understanding of the potential and challenges of LLM-generated Personas. Although the study focuses on obesity research, it highlights the importance of considering the specific context and the potential issues that researchers should be aware of when using generative AI for generating Personas.
Ključne besede: user personas, obesity, large language models, value sensitive design, digital health interventions
Objavljeno v DKUM: 14.02.2025; Ogledov: 0; Prenosov: 1
.pdf Celotno besedilo (812,18 KB)

2.
Computer science education in ChatGPT Era: experiences from an experiment in a programming course for novice programmers
Tomaž Kosar, Dragana Ostojić, Yu David Liu, Marjan Mernik, 2024, izvirni znanstveni članek

Opis: The use of large language models with chatbots like ChatGPT has become increasingly popular among students, especially in Computer Science education. However, significant debates exist in the education community on the role of ChatGPT in learning. Therefore, it is critical to understand the potential impact of ChatGPT on the learning, engagement, and overall success of students in classrooms. In this empirical study, we report on a controlled experiment with 182 participants in a first-year undergraduate course on object-oriented programming. Our differential study divided students into two groups, one using ChatGPT and the other not using it for practical programming assignments. The study results showed that the students’ performance is not influenced by ChatGPT usage (no statistical significance between groups with a p-value of 0.730), nor are the grading results of practical assignments (p-value 0.760) and midterm exams (p-value 0.856). Our findings from the controlled experiment suggest that it is safe for novice programmers to use ChatGPT if specific measures and adjustments are adopted in the education process.
Ključne besede: large language models, ChatGPT, artificial intelligence, controlled experiment, object-oriented programming, software engineering education
Objavljeno v DKUM: 12.08.2024; Ogledov: 59; Prenosov: 6
.pdf Celotno besedilo (492,37 KB)

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